---
title: "**DesignContest**"
output:
flexdashboard::flex_dashboard:
logo: 'images/rsz_grandslam.png'
theme: spacelab
storyboard: true
social: menu
source: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
```
# INTRODUCTION
Design Contest - Tennis GrandSlam Data
Dated: 10/27/2019
-Presented by
Roma Dutta
Ganesh Viswanathan
GitHub-https://github.com/ganesh2512/DSBA/tree/master/designContest_Ganesh_Roma
TidyTuesday#-spren9er
RStudioLink-https://rstudio.cloud/project/642289
```{r out.width = "100%", out.height = "100%"}
```
***
# RESEARCH
### Grand Slam results from 1968-2019 of some of the best female and male tennis players in modern era.
|
```{r out.width = "100%", out.height = "60%"}
knitr::include_graphics('images/tidytuesday_201915_tennis_grand_slams.gif')
```
|
|
- Data source -Tidy Tuesday https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-04-09
- Code Source: Dr. Torsten Sprenger's tidytusday post on Apr 9 https://github.com/spren9er/tidytuesday
https://github.com/spren9er/tidytuesday/blob/master/tidytuesday_201915_tennis_grand_slams.r
|
|
***
# CRITICAL REVIEW
### Overall The graph looks awesome -But
#### The graph provides lot of data within a single view. Its has details on winners with names along with the type of tourney and year. And is well presented.
### 1-Color Repeated
#### The color used for grandslam types and place in tourney are repeated and hence confusing the view
### 2-Color blind Issue
#### Color blind scheme could be used to make it easier for people with disabilities.
### 3-Comparison by year across players not possible
#### Comparison of how a player progressed on grandslam with his age compared to others is not possible to figure out
### 4-Arrangement of folks
#### Arrange of folks looks a little complex. Could have been better arranged as a bar with multiple axes.
### 5-Year could be in ascending order
#### The year comparison of when the player won more grandslams is not possible as arrangement is in chronological order
```{r out.width = "100%", out.height = "100%"}
```
***
# IMPROVISATION
####
|
```{r out.width = "100%", out.height = "60%"}
knitr::include_graphics('images/tidytuesday_201915_tennis_grand_slams_modified.gif')
```
|
|
- Different color options have been used for "Tournament" and "Tournament Outcome" to better comprehend
- We have applied Bang wong and Zesty color palettes that takes care of color blidness
- We have arranged the players by total number of wins
|
|
***
# PROPOSED DESIGN
|
```{r out.width = "100%", out.height = "90%"}
knitr::include_graphics('images/Capture_final.JPG')
```
|
***
# NEW IDEAS
##
```{r out.width = "50%", out.height = "50%", warning=FALSE, echo=FALSE}
library(tidyverse)
library(scales)
# Import data
player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv")
grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv")
grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv")
# Add a case statement for court types
grand_slams <-
grand_slams %>%
mutate(court_type = case_when(grand_slam == "australian_open" ~ "Hard Court",
grand_slam == "us_open" ~ "Hard Court",
grand_slam == "french_open" ~ "Clay Court",
grand_slam == "wimbledon" ~ "Grass Court"))
# Set theme for charts
#theme_set(theme_classic())
# Plot Top Ten Winners
grand_slams %>%
count(name, court_type, sort = TRUE) %>%
add_count(name, wt = n) %>%
filter(n >=5) %>%
mutate(name = fct_reorder(name, n, sum)) %>%
ggplot(aes(name, n, fill = court_type)) +
geom_col() +
scale_fill_manual(values = c('#F6BD60', '#7FB069', '#548687')) +
coord_flip() +
labs(x = "",
y = "No. of Grand Slam Wins",
title = "Top Grand Slam Winners By Court Type",
subtitle = "1968 - 2019") +
theme(legend.position="top",
legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
plot.title=element_text(size=12,face="bold"),
plot.subtitle=element_text(face="italic",size=11,colour="grey40"))
aspect_ratio <- 2
ggsave("top_players_court.png", height = 5 , width = 5 * aspect_ratio)
```
##
```{r out.width = "50%", out.height = "50%",}
library(tidyverse)
library(scales)
# Import data
player_dob <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/player_dob.csv")
grand_slams <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slams.csv")
grand_slam_timeline <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-09/grand_slam_timeline.csv")
# Calculate age
# Add a case statement for court types
grand_slams <-
grand_slams %>%
mutate(court_type = case_when(grand_slam == "australian_open" ~ "Hard Court",
grand_slam == "us_open" ~ "Hard Court",
grand_slam == "french_open" ~ "Clay Court",
grand_slam == "wimbledon" ~ "Grass Court"))
age <-
player_dob %>%
select(name, date_of_birth) %>%
inner_join(grand_slams, by = "name") %>%
mutate(age = as.numeric(difftime(tournament_date, date_of_birth, unit = "days"))/365)
# Boxplot of player ages by tournament
age %>%
mutate(grand_slam = str_to_title(str_replace(grand_slam, "_", " "))) %>%
ggplot(aes(grand_slam, age, fill = gender)) +
geom_boxplot() +
scale_fill_manual(values = c('#8700F9', '#00C4AA')) +
labs(x = "Grand Slam",
y = "Age",
title = "Distribution of Age By Grand Slam") +
theme(legend.position="top",
legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
plot.title=element_text(size=12,face="bold"))
aspect_ratio <- 2
ggsave("images/grand_slams_age_distribution.png", height = 5 , width = 5 * aspect_ratio)
```
Courtesy: https://jaredbraggins.com/2019/04/grand-slam-winners/
##
